Indoor Air Quality Dataset from a Smart Mobile Air Purification Robot in a Garment Factory

This dataset contains time-stamped environmental and operational data collected during an indoor air-quality monitoring and purification experiment carried out on the production floor of a garment factory near Dhaka, Bangladesh. The experiment was set up to reflect the kind of real-world, crowded conditions commonly found in active garment facilities — a large number of workers were present and working throughout the shared indoor space, naturally driving up particulate exposure levels and creating the fluctuating environmental conditions typical of such settings.
To capture this, a smart mobile industrial air purification robot was deployed directly on the factory floor. The robot was equipped with a PMS7003 particulate matter sensor, a DHT22 temperature and humidity sensor, and a compact HEPA-based onboard purification system, allowing it to continuously monitor the surrounding air as it navigated through the workspace. Rather than operating on a fixed schedule, the robot was programmed with sensor-based decision logic so that it could respond dynamically to degrading air-quality conditions — meaning the dataset captures not just environmental variation, but also how the robot actually behaved in response to it.
The variables recorded include timestamp, PM2.5 concentration, AQI value, AQI category, temperature, relative humidity, and bot movement status.
This dataset is intended to support research in areas such as indoor air quality in garment factory environments, smart air-purification robotics, occupational environmental monitoring, embedded sensing systems, and mobile pollution-response technologies in dense industrial workplaces.